Much of science, including biomedical science, consists of discovering and modeling causal relationships. Increasingly, biomedical scientists have available multiple complex data types and a very large number of samples, each of which has an enormous number of measurements recorded.. There is a pressing need for algorithms that can efficiently discover causal relationships from large and diverse types of biomedical data and background knowledge. In the past 25 years, tremendous progress has been made in developing general computational methods for representing and discovering causal knowledge from data. However, these methods are not readily available, nor easy to use by biomedical scientists, and they have not been designed to exploit the increasingly Big Data available for analysis. The proposed Center will create a computer ecosystem through which to implement and apply an integrated set of tools, new and repurposed, that support the representation and discovery of causal knowledge from large and complex biomedical data. These computational approaches will be accessible to a wide variety of biomedical researchers, data analysts, and data scientists who might not otherwise take advantage of them. Three very different biomedical problems will drive the development of the methods, tools, and interactive system architecture. While we anticipate that new biomedical discoveries will be made in each of these problem areas using the methods developed by the Center, the longer-term impact will result from the development of the computational technology itself, which will be generalizable to the full spectrum of biomedical research. The Center will be very active in the sharing of these knowledge, methods, and tools through a rich offering of training activities and through engagement with other Centers in the consortium.